Geospatial Analysis - Baku

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Table of Contents

  • 1.Bus Stop analyis
    • 1.1 Collecting Bus Stop Information
    • 1.2.Collecting University Information
    • 1.3.Distance analysis and Insights

  • 2.Bike Lane Analysis
    • 2.1.Collecting Bike Lane Information
    • 2.2.Loading regions dataset
    • 2.3.Length analysis and Insights

purple-divider

University - Bus-stop analaysis

Importing Libraries¶

In [118]:
import pandas as pd
from geopy.geocoders import Nominatim
from geopy.exc import GeocoderTimedOut, GeocoderUnavailable
from shapely.geometry import Point
import geopandas as gpd
import time
import requests
import osmnx as ox
import folium

Collecting Bus Stop Information¶

In [478]:
### Getting bus data
place = "Baku, Azerbaijan"
tags = {"highway": "bus_stop"}
gdf_bus = ox.features_from_place(place, tags)
In [480]:
gdf_bus = gdf_bus.to_crs("EPSG:32638")
In [499]:
gdf_bus.head()
Out[499]:
geometry bus highway name public_transport bench bin lit shelter addr:street ... covered name:signed description note outdoor_seating passenger_information_display source_ref source_ref:url addr:housenumber ref
element id
node 852866220 POINT (910555.168 4485348.981) yes bus_stop Corce Enesku parkı platform yes NaN NaN yes NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1001147686 POINT (937799.515 4494655.431) NaN bus_stop Mərdəkan \ Avtovağzal NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1213299578 POINT (908812.351 4485463.556) yes bus_stop Göz klinikası 1 platform yes no no yes NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1213299601 POINT (908461.651 4485617.557) yes bus_stop Mir Cəlal küçəsi elektrik yarımstansiyası platform NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1213339341 POINT (908511.995 4484994.026) yes bus_stop 111 nömrəli tam orta məktəb platform NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

5 rows × 37 columns

In [187]:
gdf_bus.explore()
Out[187]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Collecting University Information¶

In [400]:
tags = {"amenity": ["university", "college"]}
gdf_universities = ox.features_from_place(place, tags)

# in order to not to miss university campuses we will convert  polygons to centroids
points = gdf_universities[gdf_universities.geometry.geom_type == 'Point']
polygons = gdf_universities[gdf_universities.geometry.geom_type.isin(['Polygon', 'MultiPolygon'])]

polygons = polygons.to_crs(epsg=32638)
points = points.to_crs(epsg = 32638)
polygons['geometry'] = polygons.geometry.centroid
In [402]:
# combining all points
universities_all = pd.concat([points, polygons])
In [497]:
universities_all.head()
Out[497]:
geometry addr:city addr:country addr:housenumber addr:street amenity name name:az name:en name:ru ... height internet_access internet_access:fee description type contact:phone contact:website name:ja bus_stop_distance name_lower
element id
node 2037930732 POINT (912189 4483597.514) Bakı NaN NaN Fətəli Xan Xoyski prospekti university Azərbaycan Dövlət Bədən Tərbiyəsi və İdman Aka... Dövlət Bədən Tərbiyəsi və İdman Akademiyası NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN 177.644893 azərbaycan dövlət bədən tərbiyəsi və i̇dman ak...
2066367012 POINT (910233.594 4479498.916) Bakı AZ 31 İstiqlaliyyət university Qərbi Kaspi Universiteti Qərbi Kaspi Universiteti Western Caspian University Западно-Каспийский университет ... NaN NaN NaN NaN NaN NaN NaN NaN 28.442976 qərbi kaspi universiteti
2135629462 POINT (909615.846 4481616.159) Bakı NaN NaN Bəsti Bağırova küç. college 1 saylı Bakı Tibb Kolleci 1 saylı Bakı Tibb Kolleci NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN 96.218798 1 saylı bakı tibb kolleci
2485919776 POINT (910550.53 4484500.647) NaN NaN NaN NaN university Azərbaycan Kooperasiya Universiteti NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN 146.316574 azərbaycan kooperasiya universiteti
2485919777 POINT (910599.497 4484533.912) NaN NaN NaN NaN college Bakı Biznes və Kooperasiya Kolleci NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN 170.865342 bakı biznes və kooperasiya kolleci

5 rows × 56 columns

In [404]:
universities_all.shape
Out[404]:
(198, 54)
In [574]:
universities_all.explore(color='blue',  
                         marker_kwds={ 'radius': 2,},
                         tooltip='name')
Out[574]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Universities More Than 500 m from Bus Stops¶

In [424]:
# calculating minimum distance for universities from bus stops
universities_all['bus_stop_distance'] = universities_all.geometry.apply(
    lambda univ: univ.distance(gdf_bus.geometry).min())
In [454]:
universities_all['name_lower'] = universities_all['name'].str.lower()
In [482]:
# finding universities that fall more than 500 m from bus stops
far_universities = universities_all[universities_all['bus_stop_distance']>500]
far_universities= far_universities[['geometry','name_lower','name','bus_stop_distance']]
In [484]:
keywords = ['lisey', 'universitet', 'kollec', 'ali məktəb']
pattern = '|'.join(keywords)
filtered_df = far_universities[far_universities['name_lower'].str.contains(pattern, case=False, na=False)]

filtered_df[['name','bus_stop_distance']].sort_values(by = 'bus_stop_distance',ascending = False).reset_index(drop = True)
Out[484]:
name bus_stop_distance
0 8 saylı Bakı Peşə Liseyi 6884.756811
1 8 saylı Peşə Liseyi \ Qızıldaş 6853.965995
2 Moskva Dövlət Universitetinin Bakı Filialı 2486.837826
3 Yanğın Texniki Kolleci \ Hövsan 1488.165270
4 101 saylı Peşə Liseyi \ Binə 1280.422284
5 5 saylı Bakı Peşə Liseyi 679.200448
6 12 saylı Bakı Peşə Liseyi \ Bakıxanov 620.627620
7 Celt Kollec \ Bakıxanov 581.536615
8 1 saylı Bakı Peşə Liseyi \ Bakıxanov 544.571625
9 Xəzər Universiteti 535.178587
In [548]:
filtered_df = filtered_df.to_crs(epsg=4326)

# centering map roughly on Baku
m = folium.Map(location=[40.4093, 49.8671], zoom_start=12)

for idx, row in filtered_df.iterrows():
    lon, lat = row.geometry.x, row.geometry.y    
    folium.Marker(
        location=[lat, lon],
        popup=row['name'],
        icon=folium.Icon(icon='flag', color='red'),
        tooltip=row['name'], 

    ).add_to(m)
m.save("university_distance.html")

m
Out[548]:
Make this Notebook Trusted to load map: File -> Trust Notebook

We identified educational Institutions that are located more than 500 meters from the nearest bus stops. This highlights areas where public transportation could be more convenient for students¶

purple-divider

Bike lane analaysis

Collecting Bike Lane Information¶

In [6]:
place = "Baku, Azerbaijan"
tags = {"highway": "cycleway"}
gdf_bike_path = ox.features_from_place(place, tags)
In [8]:
gdf_bike_path = gdf_bike_path.to_crs(epsg=3857)
gdf_bike_path['length_m'] = gdf_bike_path.geometry.length
In [10]:
# getting index of max length bike line
max_bike_length = gdf_bike_path['length_m'].sort_values(ascending=False).idxmax()
max_bike_length
Out[10]:
('way', 1259359139)
In [506]:
gdf_bike_path.loc[max_bike_length].explore(style_kwds={'weight': 3, 'opacity': 1,'color':'blue'})
Out[506]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Longest bike lane is in Mardəkan-Buzovna¶

Loading regions dataset¶

Data source: https://opendata.az/

In [22]:
gdf_regions = gpd.read_file('azrbaycan_rayonlar.geojson')
gdf_regions.head(2)
Out[22]:
OBJECTID Name_AZ Name PARENT_ID Code_OSM Name_RU Area Shape_Leng Shape_Le_1 ID Shape_Le_2 Shape_Length Shape_Area Upper_case geometry
0 1 Qax Gakh 1 1204 Гах 1487.563667 3.145374 3.145374 403.0 3.145374 3.145374 0.159953 QAX POLYGON ((47.01821 41.5551, 47.01816 41.55467,...
1 2 Zaqatala Zagatala 1 1204 Закатала 1354.885693 2.853307 2.853307 402.0 2.853307 2.853307 0.146303 ZAQATALA MULTIPOLYGON (((46.47881 41.64208, 46.47853 41...
In [23]:
gdf_baku_regions = gdf_regions.loc[gdf_regions.Name_AZ.isin(['Nərimanov', 'Nizami', 'Pirallahı','Qaradağ', 'Sabunçu', 'Səbail', 'Suraxanı', 'Xətai', 'Xəzər',
                             'Yasamal', 'Binəqədi', 'Nəsimi']),['Name','geometry']].reset_index(drop = True)
In [583]:
gdf_baku_regions.explore(column = 'Name',tooltip = 'Name', style_kwds = {'fillOpacity': 0.7,'color':'gray'})
Out[583]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [28]:
gdf_baku_regions['Area'] = gdf_baku_regions.to_crs(epsg = 32638).geometry.area
In [34]:
gdf_baku_regions[['Name','Area']].sort_values(by = 'Area', ascending = False).reset_index(drop = True)
Out[34]:
Name Area
0 Garadagh 1.067115e+09
1 Khazar 3.403013e+08
2 Sabunchu 2.086572e+08
3 Surakhani 1.304281e+08
4 Binagadi 9.365016e+07
5 Pirallahi 7.991648e+07
6 Khatai 3.479748e+07
7 Sabail 2.950769e+07
8 Narimanov 2.054510e+07
9 Nizami 1.995318e+07
10 Yasamal 1.636858e+07
11 Nasimi 9.924848e+06

We see that largest district is Garadagh and smallest one is Nasimi

Total bike lane length per district in kilometers¶

In [546]:
m = gdf_baku_regions.explore(column = 'Name',color="darkblue", style_kwds={'fillOpacity': 0.5, 'color': 'gray'},)
gdf_bike_path.explore(m=m, color="darkblue",style_kwds={"weight": 3})
m.save("bike_district.html")
m
Out[546]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [46]:
districts = gdf_baku_regions.to_crs(epsg = 3857)
bike_lengths = gpd.overlay(bike_paths,districts,how = 'intersection')
In [48]:
bike_lengths.columns
Out[48]:
Index(['highway', 'crossing', 'bicycle', 'foot', 'surface', 'incline',
       'maxspeed', 'crossing:markings', 'cycleway', 'lit', 'embankment',
       'oneway', 'crossing:island', 'length_m', 'Name', 'Area', 'geometry'],
      dtype='object')
In [54]:
result = bike_lengths.groupby('Name')['length_m'].sum().reset_index()
result['length_km'] = result['length_m']/1000

result.sort_values(by = 'length_km',ascending = False).reset_index(drop = True)
Out[54]:
Name length_m length_km
0 Sabail 12690.061022 12.690061
1 Khazar 10114.345614 10.114346
2 Khatai 8308.105543 8.308106
3 Nasimi 2406.764391 2.406764
4 Yasamal 1769.567334 1.769567

Sabail District has the longest bike lane and there is one bike lane that crosses 2 different districts¶